This thesis seeks to formulate concepts and develop methods that facilitate the mining of urban big mobility data. Specifically, the aim of the formulations and developed methods is to identify and predict certain events that occur as a result of urban mobility. This thesis, studies unexpected gathering and dispersal events. A Gathering event is the process of an unusually large number of moving objects (e.g. taxi) arriving at the same area within a short period of time. It is important for city management to identify emerging gathering events which might cause public safety or sustainability concerns. Similarly, a dispersal event is the process of an unusually large number of moving objects leaving the same area within a short period of time. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. This thesis solves the problems of early detection and forecasting of gathering and predicting dispersal events. Prior work to detect gathering events uses undirected patterns which lack the ability to specify the dynamic flow of the traffic and the destination of the gathering. Forecasting gathering events is a predictive approach as apposed to descriptive approaches of detection. This thesis is the first to use destination prediction to forecast gathering events. Moreover, the presented destination prediction technique relaxes independence assumptions of related work and addresses the resulting challenges to achieve superior performance. Literature of dispersal event prediction solves this problem as a taxi demand prediction problem. Those methods aim at predicting the regular pattern and are unable to predict rare events. This thesis presents the SmartEdge Algorithm for early detection of gathering events. SmartEdge outputs a gathering footprint that specifies gathering paths and gathering destination. To forecast gathering events, this thesis presents DH-VIGO, which uses a dynamic hybrid model to forecast rare gathering events ahead of the time. Comprehensive evaluations using real-world datasets demonstrate meaningful results and superior performance compared to baseline methods. To predict dispersal events, this thesis uses a two-stage framework based on survival analysis called DILSA+, to predict the start time of the event and an event demand predictor to predict the volume of the demand in case of a dispersal event. Extensive evaluations on real-world data demonstrate that DILSA+ out-performs baselines and can effectively predict dispersal events.

In this dissertation, I examine two main topics in corporate finance: executive compensation and corporate investment. First, in the chapter titled “Import Penetration and Executive Compensation”, we investigate the impact of import penetration on executive compensation. We find that import penetration reduces executives’ total compensation, stock grants, and opportunistic grant timing, suggesting that competition mitigates agency problems and the need for conventional alignment mechanisms. Furthermore, we show that import penetration increases option grants and option duration, thus incentivizing more innovation and risk-taking. Second, I study the relationship between entrenchment and corporate investment. In the chapter titled “Entrenchment, Managerial Shirking, and Investment”, I find that entrenchment reduces capital expenditures, R&D, and productivity, weakens a firm’s competitiveness in the product market, and diminishes firm value. These findings are consistent with the shirking hypothesis that entrenchment enables managers to evade the responsibilities of overseeing investment projects.

Research has determined that measures of general mental ability (GMA) and personality are valid predictors of a wide range of work outcomes. Two of the most well established findings in the field of organizational psychology are that GMA and two of the Big Five personality traits, conscientiousness and emotional stability, predict overall job performance and training performance across all jobs. Though both GMA and personality are valid predictors of job performance, the validities of personality measures are much weaker than those observed for measures of GMA. Some argue that personality may play a larger role in predicting work outcomes than currently believed, but that current measures of personality do not capture the construct fully. Several researchers have attempted to increase the validity of personality measures by altering the items in the measures so that they refer specifically to work contexts, and others have examined the validity of observer ratings of personality. This study draws on the theory of cognitive buffering to test the possibility is that GMA itself that causes the impact of personality traits on real life performances to be limited. That is, that people may use their GMA to control the expression of their personality tendencies in their behavior. The results showed that GMA and personality interacted to predict peer ratings of personality, but not as initially hypothesized. Self-monitoring and personality also interacted to predict peer ratings of personality, but, again, not as hypothesized. Several possible explanations for the results of this study are discussed, including the notion that that individuals may make efforts to manage only those personality traits that are most relevant in given situations. Moreover, it may be the case that dispositions are less subject to the process of cognitive buffering than are emotions and affect. Limitations of this study and opportunities for future research are also discussed.

Loyalty programs for convenience stores generate consumer shopping histories that are both large in size and sparse in content. Analyzing such data with traditional basket models is computationally difficult since most models are not scalable to a large set of categories. However, analyzing large data with traditional models has important advantages: the models capture consumer (shopping) behaviors that assist managers in making strategic decisions. In this thesis, we develop two studies to analyze this large and sparse convenience store shopping data. In the first study, we bridge the gap between traditional basket model analysis and the challenges of large shopping data by developing a retail market basket modeling system that captures essential elements of consumer shopping behavior in a computationally attractive manner. An application of the model to convenience store basket data yields excellent results. The main outputs of the model (segmentation structure, cross-category dependence, price elasticities) align well with managerial intuition. Moreover, the model provides excellent forecasts to a holdout sample of consumers. Using the model, we examine the revenue impact of a change in promotion policy. In the second study, we add spatial extensions to the previous model to solve a more complex problem: retail location analysis. We develop a spatial basket model to analyze the spatial pattern of consumer heterogeneity across stores, and show how to use this model to predict the demand of a new store (without any data of consumer purchase history). The main outputs of the extended model also align well with managerial intuition. Additionally, the model provides excellent forecasts to the demand of the hold-out store.

This thesis comprises of three chapters. The first essay is titled ‘Managers: Their Effects on Accruals and Firm Policies' and is joint work with Douglas V. DeJong. The second essay is titled ‘Can the Capital Market Recognize a Manager's Financial Reporting Style?' and is sole-authored. The third essay is titled ‘Executive Compensation in a Matching Model’ and is joint work with Douglas V. DeJong, Elena Pastorino and B. Ravikumar. Chapter one investigates whether top executives have significant individual-specific effects on accruals that cannot be explained by firm characteristics. Exploiting 37 years of individual executives and firm data, we find that individual executives play a significant role in determining firms' accruals. In addition, we examine whether executives' effects on accruals are related to their personal styles in investment, financing and operating decisions. Our results show that individual executives' effects on accruals are more correlated to their operating decisions than investment and financing decisions. We also compare effects exerted by CEOs to CFOs. We find CEOs are more likely to affect accruals through firm policy decisions and CFOs are more likely to affect accruals through accounting decisions. CFOs tend to report more "solid" earnings than CEOs, i.e., CFOs are more likely to push accruals to zero. Chapter two examines whether investors can recognize idiosyncratic differences in managers' financial reporting behavior. Specifically, I investigate whether the capital market can recognize a manager's financial reporting aggressiveness and whether investors' recognition of a manager's style follows a Bayesian learning process. I use a manager's specific effect on discretionary accruals to measure her financial reporting aggressiveness. My results show that investors find earnings forecasts issued by aggressive managers to be less credible and thus respond less strongly. I also find investors follow a Bayesian learning process to identify a manager's individual style. As a manager's financial reporting history becomes longer, there is less uncertainty about the manager's true style. Consequently, the discount on the market reaction to earnings forecast news due to the manager's aggressiveness becomes larger. In sum, these results suggest that a manager's prior financial reporting history allows her to develop a financial reporting reputation, which can be inferred by investors through rationally processing historical information. Chapter three outlines our future research plan to revisit the relative importance of returns to firm-specific tenure and to general labor market experience in the labor market for executives. We shed light on the importance of explicitly accounting for an executive's firm-to-firm and job-to-job mobility, within and across firms, over the course of the executive's career in order to measure the magnitude of each type of returns.

I examine the extent to which acquirers exercise discretion in the application of FIN 48 when estimating target tax reserves. By examining the change in target tax reserves recorded through purchase accounting, I am able to hold constant the underlying tax positions, and any changes can be attributed to differences in how the managers of the target and acquirer apply the recognition and measurement principles of FIN 48. For a sample of large public-for-public M&A transactions in which the amount of target tax reserves is observable pre- and post-acquisition, approximately one third (half) of the acquirers adjust target tax reserves by more than half (a quarter) of the preexisting balance. Substantially more acquirers increase rather than decrease target tax reserves, and the average change in target tax reserves recorded through purchase accounting is $25 million. I also find evidence that the change in tax reserves recorded through purchase accounting is increasing in short-term financial reporting pressures and decreasing in the costs of overstating goodwill.

This thesis comprises of three chapters. The first essay is coauthored with Professor Matthew T. Billett and is titled ‘Asymmetric Information and Open Market Share Repurchases.' The second essay is join work with Professor Matthew T. Billett and Professor Jon A. Garfinkel and is titled ‘The Effect of Asymmetric Information on Product Market Outcomes'. The third essay is sole-authored and is titled ‘Crash Risk and Firms' Cash Policies'. Chapter one reveals cross sectional differences in undervaluation by combining open market share repurchase (OMR) announcements with asymmetric information. We find that opaque firms experience significantly larger abnormal returns than transparent firms upon an OMR. Following Ikenberry, Lakonishok an Vermaelen (1995), we strategy the sample by book-to-market, which may relate to undervaluation, and examine the effect of firm opacity within book-to-market groupings. High book-to-market opaque firms experience average three-day market-adjusted returns of 5.05% compared to 1.86% for high book-to-market transparent firms. We also document significantly positive long run post-announcement returns for opaque firms, but not for transparent firms. Our results suggest undervaluation motive for OMRs is concentrated in opaque firms, and that undervaluation due to asymmetric information attenuates at the announcement of OMRs. Chapter two explores how asymmetric information in financial markets affects outcomes in product markets. Given endogeneity concerns, we study firms in industries that experience deregulatory shocks. Post-deregulation, firms with greater opacity about their financial condition lose market share to their industry rivals. We further show that opaque firms have lower capital raising activity after deregulation. We conclude that asymmetric information in financial markets is an important determinant of product market outcomes. Chapter three examines the effect of crash risk on firms' cash policies. We find high crash risk firms which experience large negative stock returns over the fiscal year or show large conditional negative return skewness tend to hold more cash than low crash risk firms. The phenomena are more pronounced for financial constraint firms and small firms. In addition, we show that the marginal value of cash for high crash risk firms is lower compared to low crash risk firms. Based on our findings, we argue that crash risk has been taken into account when firms make their cash decisions.

This paper examines the impact of the U.S. tax and financial reporting treatment of foreign earnings on the payouts to shareholders of U.S. multinational companies (MNCs). I find the U.S. tax and financial reporting treatment of foreign earnings weakens the otherwise strong, positive association between foreign earnings and the probability and level of dividend payments, but I do not observe an effect on the probability or level of stock repurchases or on the level of total payout. I also find U.S. MNCs with tax and/or financial reporting incentives to keep their foreign profits reinvested abroad make more extensive use of repurchases than dividends when making distributions to shareholders. This study contributes to our understanding of the impact of the current U.S. worldwide tax system on U.S. MNCs' real decisions.

The term consideration set is used in marketing to refer to the set of items a customer thought about purchasing before making a choice. While consideration sets are not directly observable, finding common ones is useful for market segmentation and choice prediction. We approach the problem of inducing common consideration sets as a clustering problem. Our algorithm combines ideas from binary clustering and itemset mining, and differs from other clustering methods by reflecting the inherent structure of subset clusters. Further, we introduce two speed-up methods to make the algorithm more efficient and scalable for large datasets. Experiments on both real and simulated datasets show that our algorithm clusters effectively and efficiently even for sparse datasets. A novel evaluation method is also developed to compare clusters found by our algorithm with known ones. Based on the clusters found by our algorithm, different classification models are built for each particular consideration set. The advantages of the two-stage model are it builds specific model for different clusters, and it helps us to capture the characteristics of each group of the data by analyzing each model.

The market model is the most frequently estimated model in financial economics and has proven extremely useful in the estimation of systematic risk. In this era of rapid globalization of financial markets there has been a substantial increase in cross listings of stocks in foreign and regional capital markets. As many as a third to a half of the stocks in some major exchanges are foreign listed. The multiple listings of stocks has major implications for the estimation of systematic risk. The traditional method of estimating the market model by using data from only one market will lead to misleading estimates of beta. This study demonstrates that the estimator for systematic risk and the methodology itself changes when stocks are listed in multiple markets. General expressions are developed to obtain the estimator of global beta under a variety of assumptions about the error terms of the market models for different capital markets. The assumptions pertain both to the volatilities of the abnormal returns in each market, and to the relationship between the markets. Explicit expressions are derived for the estimation of global systematic risk beta when the returns are homoscedastic and also under different heteroscedastic conditions both within and/or between markets. These results for the estimation of global beta are further extended when return generating process follows an autoregressive scheme